4 ways to add row to existing DataFrame in Pandas

Different methods to add row to existing DataFrame in pandas

In this tutorial we will discuss how to add row to existing pandas DataFrame using the following methods (to add columns instead, see add column to existing DataFrame):

• Using loc[] function with index
• Using append() function
• Using panads.concat() function by ignoring index
• Using panads.concat() function with index.

Create pandas DataFrame with example data

DataFrame is a data structure used to store the data in two dimensional format. It is similar to table that stores the data in rows and columns. Rows represents the records/ tuples and columns refers to the attributes.

We can create the DataFrame by using pandas.DataFrame()—the pandas DataFrame guide covers construction from dicts, lists, and files before you append rows.

Syntax:

pandas.DataFrame(input_data,columns,index)

Parameters:

It will take mainly three parameters

1. input_datais represents a list of data
2. columnsrepresent the columns names for the data
3. indexrepresent the row numbers/values

We can also create a DataFrame using dictionary by skipping columns and indices.

Example: Python Program to create a dataframe for market data from a dictionary of food items by specifying the column names.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

Method 1 : Using loc[] function with index

Here we add a row with .loc[] label assignment—the loc vs iloc in pandas guide contrasts label-based .loc with integer .iloc when you pick rows elsewhere. Use len(dataframe.index) as the new row label to append at the end, then assign the row from a list.

Syntax:

dataframe.loc[len(dataframe.index)] =[list of values]

where,

1. dataframe is the input dataframe
2. list of values represents the row

Example:

Python program to add a row at last with the list of values - ['foo-45', 'wheat', 93,1]

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# add row 
dataframe.loc[len(dataframe.index)] = ['foo-45', 'wheat', 93,1] 

# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
4       foo-45           wheat   93.00         1

Method 2 : Using append() function

Here we use DataFrame.append() to add a dict-shaped row with ignore_index=True (pandas 2.x removed append; use pd.concat in Methods 3–4 or the pandas merge, concat, and append overview). Setting ignore_index=True renumbers rows—after concat, pandas reset_index shows when to keep or drop the old index as a column.

Syntax:

dataframe.append(row, ignore_index = True)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values

Example:

Python program to add a row by ignoring the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = dataframe.append(final_row, ignore_index = True)

# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   93.00         1

If we want to display the index, then we no need to specify the ignore_index parameter.

Example:Python program to display index by adding row

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])])

# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   93.00         1

Method 3 : Using panads.concat() function by ignoring index

Here we use pandas.concat() with ignore_index=True to add a row to an existing dataframe—the pandas concat guide explains when to retain, reset, or key multi-source indexes. We are ignoring the index on the appended row. We are going to append a row which is created from a dictionary and exclude the index using ignore_index parameter. It is set to True.

Syntax:

pandas.concat([dataframe,pandas.DataFrame((row),index)], ignore_index = True)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values
3. index specifies row index

Example:Python program to add a row by ignoring the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])], ignore_index = True)

# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   93.00         1

We can also add multiple rows at a time.

Example: In this example we are going to add 2 rows

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':['foo-45','foo-25'],'name': ['wheat','ragi-powder'], 'cost':[89,93],'quantity':[3,1]}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5','item-6'])], ignore_index = True)

# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   89.00         3
5  foo-25     ragi-powder   93.00         1

Method 4 : Using panads.concat() function with index

Here we are using pandas.concat() function to add a row to an existing dataframe by including the index. We are going to append a row which is created from a dictionary and include the index using ignore_index parameter. It is set to False.

Syntax:

pandas.concat([dataframe,pandas.DataFrame((row),index)], ignore_index = False)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values
3. index specifies row index

Example: Python program to add a row by including the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])], ignore_index = False)

# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   93.00         1

We can also add multiple rows at a time.

Example: In this example we are going to add 2 rows

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':['foo-45','foo-25'],'name': ['wheat','ragi-powder'], 'cost':[89,93],'quantity':[3,1]}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5','item-6'])], ignore_index = False)

# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   89.00         3
item-6  foo-25     ragi-powder   93.00         1

Summary

In this tutorial we discussed how to add row/s to an existing dataframe using append(), loc[] and concat() functions. We also included and excluded indices with concat() function. We understand that we can only add multiple rows for an existing dataframe using concat() function , but not with append() function.

References

1. Pandas - append()
2. Pandas - concat()